Path geometry based on vehicle sensing

ABSTRACT

A system receives vehicle sensor data indicating upcoming route characteristics as a vehicle travels a route. The system identifies one or more passibility-limiting features from the vehicle sensor data and, based on the sensor data, interpolates at least one characteristics of at least one feature that would affect vehicle travel. The system determines if the feature has been previously identified by comparison to previously identified features associated with locations within a proximity of a present location of the vehicle. Further, the system determines if the at least one characteristic has more than a predefined deviance from one or more previously interpolated characteristics associated with the feature and alert the user via the vehicle if at least one of the previously interpolated characteristics or the at least one characteristic indicates that the vehicle may have difficulty passing the feature based on predefined parameters of the vehicle.

TECHNICAL FIELD

The illustrative embodiments generally relate to methods and apparatusesfor mapping path geometry based on vehicle sensing.

BACKGROUND

Off-road travel has been a fun pastime for drivers who own vehiclessuited for traveling over uneven and unpaved terrain. Whether on a fixedcourse or driving through land where such travel is permitted, thesedrivers enjoy the experience of traveling bumpy, muddy, uneven terrain,as their vehicles are typically suited and tuned (or tunable) for suchtravel.

While enjoyable for these drivers, a major issue with such travel canarise when permanent obstacles (or semi-permanent obstacles) physicallyprevent travel in a manner that the vehicle cannot overcome. Forexample, two trees or large stones (permanent obstacles) may exist in aconfiguration that makes a certain vehicle body simply unable to pass,either literally or at least without damaging the vehicle exterior.Paved roads include such features far less frequently, and usually witha paved road the location and existence of such a feature (e.g., abridge height) is commonly known and does not change with time. On anoff-road course, deep water (a semi-permanent obstacle) can expand,contract and change locations, trees may grow larger, rocks may fallalongside or on the path, etc. A driver may not even be able to turnaround in certain situations, which may require significantback-tracking in reverse if an impassible object is encountered.

Satellite imagery may provide a basic sense of the trail width, but doesa poor job of delineating height-based obstacles (e.g., a tree branch)and suffers from not being able to see through obstructions (leaf-cover,cliff overhangs, etc.). Accordingly, a path may be wider, narrower, orhave less clearance than it appears to have on a satellite image.Vehicles vary widely in sizes and performance characteristics, anddetermining the possibility of a path, for a single type of vehicle, letalone a wide variety, may be a very difficult task. These trails, ifthere is even a literal trail, are often not traveled frequently enoughto make it worth the effort to record a formal map as a matter of publicrecord, and so only the drivers who travel these routes tend to know theexact nuance of a given path.

SUMMARY

In a first illustrative embodiment, a system includes one or moreprocessors configured to receive vehicle sensor data indicating upcomingroute characteristics as a vehicle travels a route. The one or moreprocessors are further configured to identify one or morepassibility-limiting features from the vehicle sensor data and, based onthe sensor data, interpolate at least one characteristics of at leastone feature that would affect vehicle travel. The processors are alsoconfigured to, based on the feature and the at least one characteristic,determine if the feature has been previously identified by comparison topreviously identified features associated with locations within aproximity of a present location of the vehicle. Further, the processorsare configured to determine if the at least one characteristic has morethan a predefined deviance from one or more previously interpolatedcharacteristics associated with the feature and alert the user via thevehicle if at least one of the previously interpolated characteristicsor the at least one characteristic indicates that the vehicle may havedifficulty passing the feature based on predefined parameters of thevehicle.

In a second illustrative embodiment, a method includes receiving arequest from a user identifying a destination and current location, therequest for travel along terrain comprising unmarked roads or trailsleading to the destination and determining previously mapped segments ofthe terrain between the current location and the destination. The methodalso includes determining based on known passibility characteristics ofthe segments and parameters associated with a vehicle driven by theuser, whether at least one projectedly passible path exists from thelocation to the destination. Further, the method includes identifying asequence of segments constituting at least one passible path orpartially passible path, depending on the results of the determiningwhether the projectedly passible path exists and presenting the sequenceto the user in the vehicle.

In a third illustrative embodiment, a method includes receiving vehiclesensor data indicating upcoming route characteristics as a vehicletravels a route and identifying one or more passibility-limitingfeatures from the vehicle sensor data. The method also includes, basedon the sensor data, interpolating at least one characteristics of atleast one feature that would affect vehicle travel and based on thefeature and the at least one characteristic, determining if the featurehas been previously identified by comparison to previously identifiedfeatures associated with locations within a proximity of a presentlocation of the vehicle. Also, the method includes determining if the atleast one characteristic has more than a predefined deviance from one ormore previously interpolated characteristics associated with the featureand alerting the user via the vehicle if at least one of the previouslyinterpolated characteristics or the at least one characteristicindicates that the vehicle may have difficulty passing the feature basedon predefined parameters of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative example of a vehicle with sensingcapability and a trail mapping system;

FIG. 2 shows an illustrative example of a process for using vehiclesensor data to geometrically map a trail;

FIG. 3 shows an illustrative example of a path-update process;

FIG. 4 shows an illustrative trail-analysis process; and

FIG. 5 shows an illustrative progress-tracking process.

DETAILED DESCRIPTION

Embodiments of the present disclosure are described herein. It is to beunderstood, however, that the disclosed embodiments are merely examplesand other embodiments can take various and alternative forms. Thefigures are not necessarily to scale; some features could be exaggeratedor minimized to show details of particular components. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a representative basis forteaching one skilled in the art to variously employ the presentinvention. As those of ordinary skill in the art will understand,various features illustrated and described with reference to any one ofthe figures can be combined with features illustrated in one or moreother figures to produce embodiments that are not explicitly illustratedor described. The combinations of features illustrated providerepresentative embodiments for typical applications. Variouscombinations and modifications of the features consistent with theteachings of this disclosure, however, could be desired for particularapplications or implementations.

In addition to having exemplary processes executed by a vehiclecomputing system located in a vehicle, in certain embodiments, theexemplary processes may be executed by a computing system incommunication with a vehicle computing system. Such a system mayinclude, but is not limited to, a wireless device (e.g., and withoutlimitation, a mobile phone) or a remote computing system (e.g., andwithout limitation, a server) connected through the wireless device.Collectively, such systems may be referred to as vehicle associatedcomputing systems (VACS). In certain embodiments, particular componentsof the VACS may perform particular portions of a process depending onthe particular implementation of the system. By way of example and notlimitation, if a process has a step of sending or receiving informationwith a paired wireless device, then it is likely that the wirelessdevice is not performing that portion of the process, since the wirelessdevice would not “send and receive” information with itself. One ofordinary skill in the art will understand when it is inappropriate toapply a particular computing system to a given solution.

Execution of processes may be facilitated through use of one or moreprocessors working alone or in conjunction with each other and executinginstructions stored on various non-transitory storage media, such as,but not limited to, flash memory, programmable memory, hard disk drives,etc. Communication between systems and processes may include use of, forexample, Bluetooth, Wi-Fi, cellular communication and other suitablewireless and wired communication.

In each of the illustrative embodiments discussed herein, an exemplary,non-limiting example of a process performable by a computing system isshown. With respect to each process, it is possible for the computingsystem executing the process to become, for the limited purpose ofexecuting the process, configured as a special purpose processor toperform the process. All processes need not be performed in theirentirety, and are understood to be examples of types of processes thatmay be performed to achieve elements of the invention. Additional stepsmay be added or removed from the exemplary processes as desired.

With respect to the illustrative embodiments described in the figuresshowing illustrative process flows, it is noted that a general purposeprocessor may be temporarily enabled as a special purpose processor forthe purpose of executing some or all of the exemplary methods shown bythese figures. When executing code providing instructions to performsome or all steps of the method, the processor may be temporarilyrepurposed as a special purpose processor, until such time as the methodis completed. In another example, to the extent appropriate, firmwareacting in accordance with a preconfigured processor may cause theprocessor to act as a special purpose processor provided for the purposeof performing the method or some reasonable variation thereof.

Drivers and vehicles traveling along trails are one of the best sourcesof information about the general characteristics of the trail andspot-characteristics. General characteristics may include, for example,widths of semi-paved portions, locations of fixed or permanentobstacles, etc. Spot-characteristics may include water-overflow,deadfall and other semi-permanent characteristics.

Using advanced vehicle sensing technology, it is possible to determinespacing between objects, object locations relative to a trail, and eventhe depth of water. This information can be reported to a globaldatabase where it can be used to more accurately geometrically map thetrail in terms of characteristics. Information from this database,provided by prior drivers of the trail, may be used as a baseline andmay be improved or modified based on presently observed characteristics.

With regards to obstacles such as water, whether they are permanent (ariver bank or lake bank) or semi-permanent (overflow) may not beimmediately apparent, but with multiple mappings it may be possible todetermine which portions of water are seemingly always there, and whichportions fluctuate with weather and seasons.

Drivers entering a trail or off-road environment can be advised ofpassable paths based on prior knowledge, semi-passable paths, orimpassible paths or areas. This analysis can be based on features of thevehicle, both performance features and body characteristics(ground-clearance, width, height, etc.). Owners or those responsible fortrails can be advised of spot-issues like deadfall, that may be cleared.By comparing a vehicle and its operating characteristics to a better setof trail parameters, vehicle operators can better work around situationswhere they may become stuck or damage to the vehicle may occur.

Traction analysis, speed, analysis, suspension analysis, etc. can helpprovide insight into the composition of a trail. Navigation systems maybe adapted to show trails, either all trails or simply passible trails.The trails could be color coded for difficulty and possibility, andusers could also set the navigation unit to only show trails of certaincharacteristics (e.g., passible without likely incident and level “easy”difficulty, as adjudged by characteristics like slopes, levelness,composition, mire, etc.). Temporal characteristics can be affixed toobstacles, which may be useful in analyzing the permanence of animpediment to travel. If an obstacle reveals itself to be amorphous innature, it may be delineated differently on a trail map, to advise auser that the obstacle may or may not be there, and a timestamp of thelast recorded parameters may be shown as well, so the user knows whenthe obstacle was last observed.

A general overview of a trail (the path) can be shown and color coded,and a more accurate segment analysis and view can be shown for a currentsegment (e.g., without limitation, the next 500 feet). Thus, the driverknows the general possibility of the trail, as well as more specificlimitations about the area upcoming. The color coding of the trail couldalso change as the driver travels, to signify the relative difficulty orpassibility of the remaining portion of the trail—i.e., if the driverwas past the only challenging part, which had resulted in the trailbeing generally classified as “difficult,” the remaining trail couldchange to a color representative of an easy trail, if only easy andeasily passible terrain remained.

FIG. 1 shows an illustrative example of a vehicle with sensingcapability and a trail mapping system. In this example, the vehicle 100is a truck with an onboard computing system 101. The computing systemincludes one or more CPUs 103, as well as a telematics control unit(TCU) 105, a BLUETOOTH transceiver 107, a Wi-Fi transceiver 109, andother appropriate communication connections. The long-range cellular TCUcan be used to communicate with the cloud, when cellular service isavailable, and can be used to transmit data about the trail to the cloudand receive data about the trail from the cloud.

Local connections may be more useful if multiple vehicles are travelingin proximity, and one leading vehicle can relay trail characteristicsback to following vehicles. This could be additionally useful if thelead vehicle was effectively mapping the trail as it went, so that thefollowing vehicles 100 would know where to travel, as well as trailcharacteristics. More permanent trails may have localized Wi-Fitransceivers (such as DSRC transceivers) provided thereto, to aid ininformation sharing and to assist in information relay when cellularservice is unavailable.

The vehicle may also include an infrared camera 111, an RGB camera 113and a variety of other sensors (e.g., without limitation, LIDAR, RADAR,SONAR, etc.). These sensors can gather information for analysis inreal-time, which can occur onboard the vehicle 100 and/or in the cloud130. The vehicle may have limited analysis capability that, for example,contemplates approximate heights, widths, etc., and the cloud may havemore robust analysis capability that is better suited for doingcomparative analysis across a wide set of data representing the trail.With sufficient computing and data access, this could also be doneonboard the vehicle 100, but there may not always be an active cellularconnection for accessing historical trail data, and the vehiclecomputing may be involved in other tasks, so the advanced processing isoften more suited for the cloud environment, but does not necessarilyhave to be done there. A marked trail with DSRC transceivers may alsoinclude an edge node that has computing and storage, and which can, forexample, keep a localized record of all the historical information abouta given trail or set of trails, so that, for example, a vehicle may haveaccess to the necessary local information, even if it cannot access thecloud dataset. This may be an installation that would be found, forexample, in an off-road section of a national park or other maintainedtrail set or area designated for trail-travel.

On the other hand, a driver may (presumably with permission) simplyelect to go driving through a random section of forest that has neverbeen traveled. Limited satellite imagery may be available, and thevehicle 100 could actively gather data about the obstacles observed andencountered. The “trail” may be a series of GPS breadcrumbs as thevehicle 100 travels, but this could establish a path for other driversand a baseline data set associated with the trail. Data can be uploadedin real-time and/or when a connection is re-established (if lost), andin such an instances onboard processing capability to do live analysisof the data may be a useful aspect, both at least because there may beno comparative data and because there may be no active or availableremote connection to the cloud in any event.

The vehicle computing system may also include range detection 115 andother analysis processes, that help determine distances to certainobjects (which can be used to determine the boundaries of objects) aswell as advisory processes 117 that provide the driver with recommendedactions based on the analysis and that may further automatically engagecertain systems to help mitigate any issues a driver might encounterbased on projected obstacles (e.g., without limitation, braking, mirrorretraction, suspension and traction changes, etc.).

The cloud 130 may include a gateway process for handling communication(incoming and outgoing). There may be a trail segment database 133,which can include both whole-trail datasets and trail segment data sets.It may be useful to segment the trail, especially where multiplebranching options occur, so that a system can dynamically assemble oneor more passible versions of a trail for a given vehicle and/ordynamically assemble one or more versions of a trail that meetpassibility or difficulty preferences of a given driver. The segmentand/or trail data 133 can include satellite imagery, trail imagery,sensor data, marked objects, permanence characteristics of objects,etc., as well as characteristics of vehicles that successfully navigatedthe trail (vehicle features, vehicle dimensions, number of times,context for travel, etc.).

Image analysis process 135 and comparable processes can analyze imageand sensor data to more clearly map the geometry and geography of atrail, and may also benefit from machine learning aspects to moreclearly identify both objects on trails and the composition or nature ofthose objects. This and other processes can also be used to update thedata sets in the segment/trail database 133, which can include adding orchanging characteristics of a trail, based on, for example, howconfident the analysis is that a certain characteristic results orexists. A route analysis process 137 can provide drivers with adviceabout local trails along a route, and may analyze the trails againstknown characteristics of a given vehicle and/or driver preferences, todetermine the suitability of a given trail for a given vehicle ordriver.

FIG. 2 shows an illustrative example of a process for using vehiclesensor data to geometrically map a trail. In this example, as a vehicle100 travels down a trail or path (or just generally forward), thevehicle 100 gathers data at 201. This can include, for example, imagerydata, sensor data, etc. For example, radar data can detect objectdistances, visual data can reveal the height and width of protrusionsthrough image analysis, this can include trail width, space betweenobjects, overhang height, etc.

As the vehicle travels forward, the data is continually gathered foreach detected object and the trail, and changes in the relativedistance, relative size, etc. are determined from dataset to dataset at203, allowing for interpolation of the locations of the objects based ontravel characteristics of the vehicle (how much the vehicle moved alongeach axis between data sets) and change characteristics of the objects.Since objects like tree branches may be moving with wind, observationover multiple sets may give information like maximum deflection (minimumheight), and other objects like tree trunks and rocks may have fixedlocation information that can be determined based on the precedingdetermination or another similar determination at 205. Thisdetermination at 205 can also include determining the height of anyprotrusions and/or width of any spaces across the trail or between twoobjects whose location has been relatively determined.

If sensors can continue to sense an object as the vehicle 100 passes theobject, this information and imagery data may be useful in determiningthe shape and size of the object, since a rock wall that is 12 feet longand a tree that is two feet wide may not reveal their difference from asensor characteristic until the vehicle passes a leading point (and cansee along a dimension of the object). Once the vehicle 100 is past theobject and any sensor data is no longer being gathered at 221, theprocess may send an estimate of the characteristic determinations forthat object to the cloud 130 at 223. This information can be combinedwith past observations about the object to form a more comprehensivepicture of the geographic and geometric state of the trail.

If the vehicle already has information about the object (e.g., from aprevious trip with data indicating the presence of the object) at 207,the process may compare the current information with prior informationat 209. If the two objects appear to match in characteristics (likelywith a certain confidence, since 100% matching may not always bepossible) at 211, the process may compare a present analysis(characteristics, location, etc.) with prior analysis at 213. If thecurrent numbers (location, height, composition, etc.) do not represent asignificant deviance at 215, the process may flag the object for apossible update at 219 and continue to gather data about the object,eventually transmitting the information to the cloud. If there is asignificant deviance, the process may alert the user at 217.

For example, a rock may appear to have substantially the similar shapeand location as a prior rock, in one instance, so while additionalmapping may better define the rock, the passibiltiy of the trail mayremain unaffected. In another example, additional rocks may have fallen,and the rock may seem to only share about 40 or 50% of thecharacteristics expected of a rock at that location (the deviancethreshold can be user or OEM tunable) and this may trigger a user alertto at least slow down, as the possibility determination was based on theprior condition of the trail (i.e., the rock as it previously was).Similarly a tree may grow slightly or move in the wind (similarconditions) or a large portion of the tree may break and fall in a storm(significant deviance) and the user may find a route impassible becauseof a characteristic change. If the vehicle 100 detects any objectseither not previously encountered or unexpectedly different from dataused for a passibiltiy calculation, it may alert the user to slow down,so that the user does not move forward assuming that the way may betight, but it generally passible, since that determination may beaffected by the changed characteristics or new object.

FIG. 3 shows an illustrative example of a path-update process. In thisexample, the process receives known prior segments of trail dataproximate to a vehicle 100 location at 301. This may include, forexample, all known segments of trail within a planned travelingdistance, so that if a signal is later lost, local trail segments are atleast onboard the vehicle. If the current or upcoming segment is a priorsegment at 303, previously observed, then the vehicle 100 may use 1 Hzradar and camera data for comparison against a width profile at 305. 1Hz radar is an example of a scan that may be less detailed than anotheroption, such as 10 Hz, but the two particular frequencies are used forillustrative purposes only in a relativistic sense.

On the other hand, if the upcoming segment is new (or includes likelynew sections, for a random path through the woods, for example), theprocess may use 10 Hz camera and radar data for mapping the section forinitial recordation at 307. This or other higher resolution scanning maybe useful on newer trail segments where less complete data exists. Evenif the segment has been previously observed, higher frequency, higherdetail mapping may be used for some number of intervals or until certainresolutions of information have been obtained and confirmed Additionallyor alternatively, a lower resolution scan resulting in significantdeviance may result in a request for a higher resolution scan from thevehicle, or from a next-vehicle if the vehicle reporting the deviance isalready past a point of interest.

The results are added to the data store at 309, which can includeimmediate or later upload if connections are not readily available. Theresults for new mappings are added as new data, and the results forrelative comparison may be added as modifications to existing data. Whenan object appears to have, for example, multiple different locationsproximate to a trail, systems may elect to use the seemingly closestlocation for the object until later confirmation data can more preciselyfix the location relative to the trail. This may over-define the objectperimeter, but may be a better approach from a driver perspective than,for example, using an average of the observed locations, which is also apossible solution.

For each dataset observed through the sensors, the vehicle 100 mayappend telemetry data, chassis signal logs, etc. at 311, so that vehicledata carries forward with the object data, which may be useful in lateranalysis. This data can then be shared with the cloud at 313.

FIG. 4 shows an illustrative trail-analysis process. In this example,the process sends a present location or start location and a destination(e.g., trail end) at 401. There may be a single defined trail betweenthe two locations, or there may be more of a sandbox, where a user cango many directions. In the latter case, groups of GPS coordinates orknown trail portions may form segments. Segments may also be delineatedby areas where branching occurs and a choice can be made, or by areasthat have markedly different travel characteristics—e.g., on a five miletrail, the first two miles may lead to a narrow ravine of 500 feet, andthose may be two different segments even if the trail only has asingular route.

The server may look up various segments of the trail at 403, as notedabove. There may only be a single segment, or a single route may bedivided into segments. Here, the analysis is a possibility analysis, atleast, and so the characteristics of a segment may include theleast-passible natures of the segment. For example, if one portion ofthe segment has large holes, and another is very narrow, the correctclearances and vehicle widths necessary for passing the segment may bedefined in relationship to the segment, which can be, among otherthings, defined in terms of the actual characteristic (e.g., holes of 8inches and 12 inches wide/width of 5 feet/clearance of 7 feet) or interms of vehicle characteristics (e.g., minimum ground clearance,maximum width and height).

If there is not a path from start to finish for which allcharacteristics are known at 405, so which is ultimately projected to beimpassible, the process may determine a “best” path at 407 and at leastwhich segments are clear at 409. These could be shown to the user as apath in green for the best and clear alternative branches as yellow, forexample. The process may also identify blocked, impassible or unknownsegments at 411, which could be shown to the user as red branches. Thesemay not be fully blocked, but may be deemed to be impassible for variousreasons and so represent options that should not likely be taken by theuser. The process can then send the results at 413 for presentation to auser, for example. The user may still want results representing the mostprojected travelable space, even if the ultimate conclusion is that theuser cannot likely clear the route.

If there is a complete path found, the process saves the identifiedsegments at 415 that represent possible complete paths, which can alsoinclude difficult segments that can be presented to a user aschallenging, but likely passible with sufficient skill. Certain segmentsmay be impassible at 417, such as those through which a vehicle simplywill not fit (e.g., a 4 foot wide clearance) and those can be save foridentification for the user, along with the characteristics that renderthem projected impassible, at 419.

Other segments may be deemed difficult at 421, such as those that arebarely physically passible or those requiring advanced driving skills orcareful maneuvering to work around obstacles. Those can be saved at 423,along with a list at 425 of potential issues, recommended skills, etc.If the segment is a longer segment, specific coordinates of projecteddifficult travel can also be identified, and/or the segment could becolor coded in multiple colors to identify for a user when they areapproaching a difficult point. The data on possible routes and segmentcharacteristics can then be returned at 427.

FIG. 5 shows an illustrative progress-tracking process. This allowspresentation of a possible route for users, along with segment-specificinformation that can also be improved as above as the user travels asegment or trail.

The vehicle receives the segments and/or full route(s) at 501 anddisplays possible paths at 503. This can include a recommended path orpath, challenging paths, and impassible paths, which may be delineatedby color or other scheme. A user can select a preferred path at 505,which can be selection of whole path or individual segment selection.

As the vehicle travels, the process determines if a new segment is beingapproached at 507. Data for a current and/or next segment, for example,may be displayed to the user. Any alerts associated with the segment maybe displayed at 509. This can include, for example, passibility alerts,driving skill alerts, high maneuvering alerts, etc. Any recommendationsmay also be displayed at 511, which can include, for example, speedlimiting, retraction of windows or upper lighting mounts, use of ornon-use of mode settings, etc. The vehicle tracks the progress of theuser along the segments and can provide more detailed information asneeded when a particular point of a segment is reached or approached.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms encompassed by the claims.The words used in the specification are words of description rather thanlimitation, and it is understood that various changes can be madewithout departing from the spirit and scope of the disclosure. Aspreviously described, the features of various embodiments can becombined to form further embodiments of the invention that may not beexplicitly described or illustrated. While various embodiments couldhave been described as providing advantages or being preferred overother embodiments or prior art implementations with respect to one ormore desired characteristics, those of ordinary skill in the artrecognize that one or more features or characteristics can becompromised to achieve desired overall system attributes, which dependon the specific application and implementation. As such, embodimentsdescribed as less desirable than other embodiments or prior artimplementations with respect to one or more characteristics are notoutside the scope of the disclosure and can be desirable for particularapplications.

What is claimed is:
 1. A system comprising: one or more processorsconfigured to: receive vehicle sensor data indicating upcoming routecharacteristics as a vehicle travels a route; identify one or morepassibility-limiting features from the vehicle sensor data; based on thesensor data, interpolate at least one characteristics of at least onefeature that would affect vehicle travel; based on the feature and theat least one characteristic, determine if the feature has beenpreviously identified by comparison to previously identified featuresassociated with locations within a proximity of a present location ofthe vehicle; determine if the at least one characteristic has more thana predefined deviance from one or more previously interpolatedcharacteristics associated with the feature; and alert the user via thevehicle if at least one of the previously interpolated characteristicsor the at least one characteristic indicates that the vehicle may havedifficulty passing the feature based on predefined parameters of thevehicle.
 2. The system of claim 1, wherein the data includes radar data.3. The system of claim 2, wherein the radar data is requested by the oneor more processors at a varied granularity based on whether or not acurrent vehicle path, determined at least in part by a vehicle location,has been previously identified and mapped for route characteristics. 4.The system of claim 1, wherein the characteristics include height. 5.The system of claim 1, wherein the characteristics include width.
 6. Thesystem of claim 1, wherein the characteristics include depth.
 7. Thesystem of claim 1, wherein the characteristics include location.
 8. Thesystem of claim 1, wherein the parameters include ground clearance. 9.The system of claim 1, wherein the parameters include vehicle width orheight.
 10. A method comprising: receiving a request from a useridentifying a destination and current location, the request for travelalong terrain comprising unmarked roads or trails leading to thedestination; determining previously mapped segments of the terrainbetween the current location and the destination; determining based onknown passibility characteristics of the segments and parametersassociated with a vehicle driven by the user, whether at least oneprojectedly passible path exists from the location to the destination;and identifying a sequence of segments constituting at least onepassible path or partially passible path, depending on the results ofthe determining whether the projectedly passible path exists; andpresenting the sequence to the user in the vehicle.
 11. The method ofclaim 10, wherein the passibility characteristics include route width.12. The method of claim 10, wherein the passibility characteristicsinclude route height.
 13. The method of claim 10, wherein thepassibility characteristics include at least one of height, width ordepth of an obstacle.
 14. The method of claim 10, wherein the vehicleparameters include ground clearance.
 15. The method of claim 10, whereinthe vehicle parameters include vehicle width and height.
 16. The methodof claim 10, further comprising identifying previously mapped segmentsof the terrain determined to be projectedly impassible.
 17. The methodof claim 10, further comprising identifying and presentingcharacteristics predefined as creating potentially difficult drivingassociated with at least one identified segment.
 18. The method of claim17, further comprising identifying the characteristics based on thevehicle parameters compared to the known possibility characteristics.19. The method of claim 17, further comprising provided enhancedinformation, including at least characteristic values or recommendationsfor proceeding, in the vehicle, responsive to determining that thevehicle is within a predefined proximity to at least one identifiedcharacteristic predefined as creating potentially difficult driving. 20.A method comprising: receiving vehicle sensor data indicating upcomingroute characteristics as a vehicle travels a route; identifying one ormore passibility-limiting features from the vehicle sensor data; basedon the sensor data, interpolating at least one characteristics of atleast one feature that would affect vehicle travel; based on the featureand the at least one characteristic, determining if the feature has beenpreviously identified by comparison to previously identified featuresassociated with locations within a proximity of a present location ofthe vehicle; determining if the at least one characteristic has morethan a predefined deviance from one or more previously interpolatedcharacteristics associated with the feature; and alerting the user viathe vehicle if at least one of the previously interpolatedcharacteristics or the at least one characteristic indicates that thevehicle may have difficulty passing the feature based on predefinedparameters of the vehicle.